def create_hyperopt_files(experiments_file_name):
## Get templates and Variants
config_strings = create_config_strings(experiments_file_name)
config_objects = create_config_objects(config_strings)
templates, variants = create_templates_variants_from_config_objects(
config_objects)
## Create template string and save to file
template_str = "{\n templates: {\n"
for key in templates:
template_str += " {:s}: {:s},\n".format(key, templates[key])
template_str += "}}\n\n"
with open('hyperopt_template.yaml', 'w') as template_file:
template_file.write(template_str)
## Create parameter ranges and save to .pcs-file
# Fold param variants into one dict param -> list of possibilities
parameter_ranges = dict()
for param_dict in variants:
for key in param_dict:
if key in parameter_ranges and (param_dict[key]
not in parameter_ranges[key]):
parameter_ranges[key].append(param_dict[key])
if key not in parameter_ranges:
parameter_ranges[key] = [param_dict[key]]
# Delete unnecessary stuff, add template name reminder
parameter_ranges.pop('dataset_filename')
parameter_ranges.pop('save_path')
parameter_ranges['template_name'] = ['!ADD_TEMPLATE_FILE_NAME!']
# Build string
hyperopt_param_string = ""
for key, values in parameter_ranges.iteritems():
# take middle value as default value
default_str = "[{:s}]".format(str(values[len(values) // 2]))
if len(values) == 1:
val_str = "{{{:s}}}".format(str(values[0]))
else:
is_integer = False
if all(isinstance(val, numbers.Number) for val in values):
val_str = "[{:s}, {:s}]".format(str(values[0]),
str(values[-1]))
is_integer = all(val.is_integer() for val in values)
if (is_integer):
default_str += 'i'
else:
val_str = str(values).replace('(', '{').replace(')', '}')
line = "{:30s} {:30s} {:s}\n".format(str(key), val_str, default_str)
line = line.replace("$", "**")
# correct indentation
line = line.replace(" [**", "[**")
hyperopt_param_string += line
with open('hyperopt_params.pcs', 'w') as param_file:
param_file.write(hyperopt_param_string)
def create_hyperopt_files(experiments_file_name):
## Get templates and Variants
config_strings = create_config_strings(experiments_file_name)
config_objects = create_config_objects(config_strings)
templates, variants = create_templates_variants_from_config_objects(config_objects)
## Create template string and save to file
template_str = "{\n templates: {\n"
for key in templates:
template_str += " {:s}: {:s},\n".format(key, templates[key])
template_str += "}}\n\n"
with open('hyperopt_template.yaml', 'w') as template_file:
template_file.write(template_str)
## Create parameter ranges and save to .pcs-file
# Fold param variants into one dict param -> list of possibilities
parameter_ranges = dict()
for param_dict in variants:
for key in param_dict:
if key in parameter_ranges and (param_dict[key] not in parameter_ranges[key]):
parameter_ranges[key].append(param_dict[key])
if key not in parameter_ranges:
parameter_ranges[key] = [param_dict[key]]
# Delete unnecessary stuff, add template name reminder
parameter_ranges.pop('dataset_filename')
parameter_ranges.pop('save_path')
parameter_ranges['template_name'] = ['!ADD_TEMPLATE_FILE_NAME!']
# Build string
hyperopt_param_string = ""
for key, values in parameter_ranges.iteritems():
# take middle value as default value
default_str = "[{:s}]".format(str(values[len(values) // 2]))
if len(values) == 1:
val_str = "{{{:s}}}".format(str(values[0]))
else:
is_integer = False
if all(isinstance(val, numbers.Number) for val in values):
val_str = "[{:s}, {:s}]".format(str(values[0]), str(values[-1]))
is_integer = all(val.is_integer() for val in values)
if (is_integer):
default_str += 'i'
else:
val_str = str(values).replace('(', '{').replace(')', '}')
line = "{:30s} {:30s} {:s}\n".format(str(key), val_str, default_str)
line = line.replace("$", "**")
# correct indentation
line = line.replace(" [**", "[**")
hyperopt_param_string += line
with open('hyperopt_params.pcs', 'w') as param_file:
param_file.write(hyperopt_param_string)
def train_hyperopt(params):
""" Runs one fold with given parameters and returns test misclass."""
lasagne.random.set_rng(RandomState(9859295))
template_name = params.pop('template_name')
params = adjust_params_for_hyperopt(params)
config_strings = create_config_strings(template_name)
config_objects = create_config_objects(config_strings)
templates, _ = create_templates_variants_from_config_objects(
config_objects)
processed_templates, params_without_template_params = process_templates(
templates, params)
final_params = process_parameters_by_templates(
params_without_template_params, processed_templates)
# go to directory above this source-file
main_template_filename = os.path.dirname(
os.path.abspath(os.path.dirname(__file__)))
# then complete path to config
main_template_filename = os.path.join(main_template_filename, "configs",
"eegnet_template.yaml")
with (open(main_template_filename, 'r')) as main_template_file:
main_template_str = main_template_file.read()
final_params['original_params'] = 'dummy'
train_str = Template(main_template_str).substitute(final_params)
def do_not_load_constructor(loader, node):
return None
yaml.add_constructor(u'!DoNotLoad', do_not_load_constructor)
modified_train_str = train_str.replace('layers: ', 'layers: !DoNotLoad ')
train_dict = yaml_parse.load(modified_train_str)
dataset = train_dict['dataset']
dataset.load()
dataset_provider = train_dict['dataset_provider']
assert 'in_sensors' in train_str
assert 'in_rows' in train_str
assert 'in_cols' in train_str
train_str = train_str.replace('in_sensors',
str(dataset.get_topological_view().shape[1]))
train_str = train_str.replace('in_rows',
str(dataset.get_topological_view().shape[2]))
train_str = train_str.replace('in_cols',
str(dataset.get_topological_view().shape[3]))
train_dict = yaml_parse.load(train_str)
layers = train_dict['layers']
final_layer = layers[-1]
# turn off debug/info logging
logging.getLogger("pylearn2").setLevel(logging.WARN)
logging.getLogger("braindecode").setLevel(logging.WARN)
exp = Experiment()
exp.setup(final_layer, dataset_provider, **train_dict['exp_args'])
exp.run()
final_misclass = exp.monitor_chans['test_misclass'][-1]
print("Result for")
pprint(params)
print("Final Test misclass: {:5.4f}".format(float(final_misclass)))
return final_misclass
def train_hyperopt(params):
""" Runs one fold with given parameters and returns test misclass."""
lasagne.random.set_rng(RandomState(9859295))
template_name = params.pop('template_name')
params = adjust_params_for_hyperopt(params)
config_strings = create_config_strings(template_name)
config_objects = create_config_objects(config_strings)
templates, _ = create_templates_variants_from_config_objects(
config_objects)
processed_templates, params_without_template_params = process_templates(
templates, params)
final_params = process_parameters_by_templates(params_without_template_params,
processed_templates)
# go to directory above this source-file
main_template_filename = os.path.dirname(os.path.abspath(os.path.dirname(
__file__)))
# then complete path to config
main_template_filename = os.path.join(main_template_filename, "configs",
"eegnet_template.yaml")
with (open(main_template_filename, 'r')) as main_template_file:
main_template_str = main_template_file.read()
final_params['original_params'] = 'dummy'
train_str = Template(main_template_str).substitute(final_params)
def do_not_load_constructor(loader, node):
return None
yaml.add_constructor(u'!DoNotLoad', do_not_load_constructor)
modified_train_str = train_str.replace('layers: ', 'layers: !DoNotLoad ')
train_dict = yaml_parse.load(modified_train_str)
dataset = train_dict['dataset']
dataset.load()
dataset_provider = train_dict['dataset_provider']
assert 'in_sensors' in train_str
assert 'in_rows' in train_str
assert 'in_cols' in train_str
train_str = train_str.replace('in_sensors',
str(dataset.get_topological_view().shape[1]))
train_str = train_str.replace('in_rows',
str(dataset.get_topological_view().shape[2]))
train_str = train_str.replace('in_cols',
str(dataset.get_topological_view().shape[3]))
train_dict = yaml_parse.load(train_str)
layers = train_dict['layers']
final_layer = layers[-1]
# turn off debug/info logging
logging.getLogger("pylearn2").setLevel(logging.WARN)
logging.getLogger("braindecode").setLevel(logging.WARN)
exp = Experiment()
exp.setup(final_layer, dataset_provider, **train_dict['exp_args'])
exp.run()
final_misclass = exp.monitor_chans['test_misclass'][-1]
print("Result for")
pprint(params)
print("Final Test misclass: {:5.4f}".format(float(final_misclass)))
return final_misclass